DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 24 April 2026 has been entered.
Drawings
The applicant’s drawings submitted are acceptable for examination purposes.
Response to Arguments
Applicant's arguments filed 24 April 2026 have been fully considered but they are not persuasive. Please see response to arguments below in the present Office action.
In response to the applicant's argument that "Claims 13-24 stand rejected under 35 U.S.C.112(b), as "pre-set vessel diameter" is not defined. See 01/18/2026 Office Action at pp. 2-4. Applicant respectfully disagrees…"Not greater than preset vessel diameter" in some embodiments means diameters equal to or smaller than 50 μm," the Examiner traverses. The cited examples and embodiments in the as-filed specification do not cure the indefiniteness of Claim 13. Independent Claim 13 itself does not define the pre-set vessel diameter, the manner in which it is selected, or the objective criteria for determining whether a vessel is greater than or not greater than the preset vessel diameter. Although certain embodiments may describe 50 μm as a non-limiting example, those disclosures do not provide objective boundaries for the full scope of independent Claim 13, which remains broad enough to encompass arbitrary and variable thresholds. Furthermore, Claim 13 still fails to define what constitutes a vessel direction, how such direction is measured, or how the small vessel annotation result is evaluated relative to the preset vessel diameter, such that the metes and bounds remain unclear. Examiner reminds the applicant that “During patent examination, the pending claims must be “given their broadest reasonable interpretation consistent with the specification.” The Federal Circuit’s en banc decision in Phillips v. AWH Corp., 415 F.3d 1303, 1316, 75 USPQ2d 1321, 1329 (Fed. Cir. 2005) expressly recognized that the USPTO employs the “broadest reasonable interpretation” standard” and “Because applicant has the opportunity to amend the claims during prosecution, giving a claim its broadest reasonable interpretation will reduce the possibility that the claim, once issued, will be interpreted more broadly than is justified. In re Yamamoto, 740 F.2d 1569, 1571 (Fed. Cir. 1984); In re Zletz, 893 F.2d 319, 321, 13 USPQ2d 1320, 1322 (Fed. Cir. 1989) (“During patent examination the pending claims must be interpreted as broadly as their terms reasonably allow.”); In re Prater, 415 F.2d 1393, 1404-05, 162 USPQ 541, 550-51 (CCPA 1969)” See MPEP § 2111. See 112(b) rejection(s) below for further details and guidance.
In response to the applicant's argument that ‘"a small vessel" annotation result in some embodiments is formed by annotating a direction of a vessel with a vessel diameter not greater than 50 μm. As such, allowance of claims 13-24 is respectfully requested,’ the Examiner traverses. Again, the cited examples and embodiments in the as-filed specification do not cure the indefiniteness of Claim 13. Even if certain embodiments describe annotating a vessel direction for vessels having diameters not greater than 50 μm, Claim 13 still fails to provide objective boundaries for what constitutes a small vessel annotation result. The term “small” remains relative and subjective, for the claim does not define how the annotation result is measured, quantified, or evaluated, nor does it specify the degree to which the annotation result must correspond to vessels below the threshold diameter. Thus, a person having ordinary skill in the art would still be unable to determine the metes and bounds of the claimed small vessel annotation result with reasonable certainty. Examiner reminds the applicant that ‘“Though understanding the claim language may be aided by explanations contained in the written description, it is important not to import into a claim limitations that are not part of the claim. For example, a particular embodiment appearing in the written description may not be read into a claim when the claim language is broader than the embodiment.” Superguide Corp. v. DirecTV Enterprises, Inc., 358 F.3d 870, 875, 69 USPQ2d 1865, 1868 (Fed. Cir. 2004).’ See MPEP § 2111. See 112(b) rejection(s) below for further details and guidance.
In response to the applicant's argument that "Neither Jin nor Badawi teaches the claimed arteriovenous vessel annotation scheme…Therefore, neither reference alone or in combination can supply the claimed limitation of vessel-diameter-dependent annotation scheme," the Examiner traverses. Examiner reminds the applicant that the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981). It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method for determining the diameter of arteries and veins in a fundus image of Jin to further specify the technical features of weighted multi-task segmentation of retinal vessels through a multiloss function optimized deep encoder-decoder/CNN based design for classifying retinal image pixels into arterioles and venules, for the purpose of targeting better optimization of vessel classification, increasing accurate results, reducing bias in sensitivity, fixing artery segment pixels that are wrongly judged as vein pixels, and fixing vein pixels that are judged as arteries in deep learning, as taught by Badawi (pg. 3, col. 2, para. 2-3 and pg. 5, col. 1, para. 4). Furthermore, “If a prima facie case of obviousness is established, the burden shifts to the applicant to come forward with arguments and/or evidence to rebut the prima facie case. See, e.g., In re Dillon, 919 F.2d 688, 692, 16 USPQ2d 1897, 1901 (Fed. Cir. 1990) (en banc).” See MPEP § 2145. Counsel's assertion is merely an argument unaccompanied by evidentiary support, and, thus, is insufficient to rebut Examiner's finding of obviousness. Arguments of counsel cannot take the place of evidence in the record. In re Schulze, 346 F.2d 600, 602, 145 USPQ 716, 718 (CCPA 1965); In re Geisler, 116 F.3d 1465, 43 USPQ2d 1362 (Fed. Cir. 1997). MPEP § 2145, 716.01(c). Jin in view of Badawi teaches all limitations of Claim 13, as recited in the Final Office action dated 28 January 2026. See 103 and 112(b) rejection(s) below for further details and guidance.
In response to the applicant's argument that "Badawi's "pixel-to-pixel similarity" cannot be mapped to "annotating a boundary"…Therefore, Badawi cannot supply the claimed limitation of boundary-specific annotation," the Examiner traverses. Applicant’s argument focuses on the pixel-to-pixel similarity calculation while ignoring Badawi’s vessel segmentation and AV classification results (semantic segmentation of retinal vessels simultaneously attaining grouping of arteries and veins; pg. 3, col. 2, para. 2, achieved semantic classification of vessels to arteries and veins by assigning each pixel of retinal image a class label e.g., arteriole, venule, or background pixel; pg. 3, col. 2, para. 2, within retinal image; pg. 3, col. 2, para. 2; retinal fundus images for AV classification; pg. 6, col. 2, para. 2, via deep learning result comprising AV classification dataset; pg. 3, col. 2, para. 3, comprising AV classification dataset comprising achieved semantic classification of vessels to arteries and veins; pg. 3, col. 2, para. 2-3, and class label e.g., arteriole and venule, gold standard labels of AV classification and vessel segmentation; pg. 3, col. 2, para. 2-3; Badawi; as recited in the Final Office action dated 28 January 2026). Badawi’s semantic classification of vessels into arteries and veins, together with vessel segmentation and labeled vessel regions, requires identification of vessel structures and their corresponding edges within the image. A vessel boundary is defined by the transition between vessel pixels and non-vessel pixels in the segmented image (achieved semantic classification of vessels to arteries and veins by assigning each pixel of retinal image a class label e.g., arteriole, venule, or background pixel; pg. 3, col. 2, para. 2, pixel-to-pixel similarity measure, achieved by comparing pixel from segmented image to same pixel in ground truth and building matrix and performing calculation of desired performance measures; pg. 5, col. 1, para, 2-3; Badawi; as recited in the Final Office action dated 28 January 2026). Thus, even if Badawi evaluates performance using pixel-wise similarity metrics or loss functions, these calculations are performed on annotated vessel regions whose boundaries have already been identified through the segmentation and classification process that Badawi explicitly discloses. Examiner submits that Claim 13 does not require an explicit boundary-only loss calculation, and therefore Badawi’s annotated segmented vessel regions corresponding to arteries and veins reasonably teaches annotating a boundary of a vessel. Examiner further reminds the applicant that “The elements must be arranged as required by the claim, but this is not an ipsissimis verbis test, i.e., identity of terminology is not required. In re Bond, 910 F.2d 831, 15 USPQ2d 1566 (Fed. Cir. 1990).” See MPEP § 2131.
In response to the applicant's argument that "Badawi's "thin-vessel analysis" is not the same as "small vessel annotation"…Therefore, Badawi cannot supply the claimed limitation of direction-specific annotation and small-vessel-annotation based loss function weight adjustment. As such, claim 13 is allowable and allowance of claim 13 is respectfully requested," the Examiner traverses. In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., "using direction-specific annotation to adjust loss function weights to control the contribution of a region corresponding to the small vessel annotation result and its vicinity" and "direction-specific annotation") are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). In response to applicant's argument that "Badawi does not:...using direction-specific annotation to adjust loss function weights to control the contribution of a region corresponding to the small vessel annotation result and its vicinity" , the fact that the inventor has recognized another advantage which would flow naturally from following the suggestion of the prior art cannot be the basis for patentability when the differences would otherwise be obvious. See Ex parte Obiaya, 227 USPQ 58, 60 (Bd. Pat. App. & Inter. 1985). Badawi expressly identifies thin vessels based on vessel width, performs pixel-level semantic segmentation and AV classification, and adjusts the loss function through segment-wise contextual judgement to improve classification accuracy for thin vessels (semantic segmentation of retinal vessels simultaneously attaining grouping of arteries and veins; pg. 3, col. 2, para. 2, achieved semantic classification of vessels to arteries and veins by assigning each pixel of retinal image a class label e.g., arteriole, venule, or background pixel; pg. 3, col. 2, para. 2, within retinal image; pg. 3, col. 2, para. 2; retinal fundus images for AV classification; pg. 6, col. 2, para. 2, via deep learning result comprising AV classification dataset; pg. 3, col. 2, para. 3, comprising AV classification dataset comprising achieved semantic classification of vessels to arteries and veins; pg. 3, col. 2, para. 2-3, and class label e.g., arteriole and venule, gold standard labels of AV classification and vessel segmentation; pg. 3, col. 2, para. 2-3; thin vessels in labelled image defined as vessels that are less than four-pixel width; pg. 5, col. 1, para, 2-3; adding segment-wise contextual judgment to loss function that optimizes vessel classification, applying optimized deep learning method on newly prepared AV classification dataset, improving accuracy of labels annotated as thin vessels, i.e., deep learning-based pixel level semantic segmentation utilized in classifying retinal blood vessels into arterioles/venules; pg. 2, col. 1, para. 4; Badawi; as recited in the Final Office action dated 28 January 2026). Since vessel segmentation identifies vessel structure and orientation within the image, the classified thin vessel regions of Badawi correspond to annotated vessel directions and regions associated with vessels below a threshold diameter. Examiner notes that Claim 13 does not require any specific mathematical form of weight adjustment or a distinct directional weighting algorithm. Thus, Badawi’s disclosure of thin-vessel specific contextual weighting and optimization of the loss function for segmented thin vessel regions teaches adjusting the weight of a region corresponding to a small vessel annotation result. Examiner submits that the fact that Badawi discloses correcting artery/vein misclassification at the segment level does not negate that the loss function weighting is applied based on identified thin-vessel regions corresponding to the claimed small vessel annotation result.
In response to the applicant's argument that "Claims 14-24 depend from claim 13 and are allowable both for depending from an allowable base claim and for limitations they each introduce. As such, claims 14-24 are allowable and allowance of claims 14- 24 is respectfully requested," the Examiner traverses. Applicant's arguments do not comply with 37 CFR 1.111(c) because they do not clearly point out the patentable novelty which they think the claims present in view of the state of the art disclosed by the references cited or the objections made. Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 13-24 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With respect to Claim 13, the limitations “a boundary of a vessel with a vessel diameter greater than a pre-set vessel diameter" and "a small vessel annotation result formed by annotating a direction of a vessel not greater than the pre-set vessel diameter” have relative terms which render the claim indefinite. The phrases “vessel diameter greater than a pre-set vessel diameter" and "a small vessel annotation result” are not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention.
These limitations are indefinite because the pre-set vessel diameter is not defined, nor is it recited who or what sets it, how it is determined, or under what conditions, and thus, leaves the threshold arbitrary and variable. It is also unclear since the limitations utilize relative comparison (e.g., “greater than,” “not greater than,” “a small vessel,” etc.) without specifying what the vessel direction refers to, how it is measured, or how it relates to vessel diameter. The pre-set value, measurement methodology, diameter boundaries, and evaluation conditions are also not defined, for there is no way to determine how small the annotation result must be or how much greater the vessel boundary must be relative to the pre-set diameter. Thus, a person having ordinary skill in the art cannot objectively determine which vessels qualify, how annotations are formed, or where the claim boundary lies. Since the scope cannot be ascertained, the claims are indefinite under § 112(b). Furthermore, the arteriovenous vessel annotation results, artery annotation result, vein annotation result, boundary of a vessel, vessel diameter, a direction of a vessel, loss function, and a weight of a region being adjusted are also rendered indefinite by the use of the terms “pre-set vessel diameter” and “small vessel annotation result.”
For the prosecution on merits, examiner interprets the claimed subject matter described above as introducing optional elements, optional structural limitations, optional expressions, and optional functionality within a method for measuring lesion features of hypertensive retinopathy.
Applicant should clarify the claim limitations as appropriate. Care should be taken during revision of the description and of any statements of problem or advantage, not to add subject-matter which extends beyond the content of the application (specification) as originally filed.
If the language of a claim, considered as a whole in light of the specification and given its broadest reasonable interpretation, is such that a person of ordinary skill in the relevant art would read it with more than one reasonable interpretation, then a rejection of the claims under 35 U.S.C. 112, second paragraph, is appropriate. See MPEP 2173.05(a), MPEP 2143.03(I), and MPEP 2173.06.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 13-24 are rejected under 35 U.S.C. 103 as being unpatentable over Jin et al. CN 111681276 A (see machine translation; herein after "Jin") in view of Badawi SA, Fraz MM. Multiloss Function Based Deep Convolutional Neural Network for Segmentation of Retinal Vasculature into Arterioles and Venules, Biomed Res Int. 2019 Apr 14; pgs. 1-17 (herein after "Badawi").
With respect to Claim 13, Jin discloses a method for measuring lesion features of hypertensive retinopathy (method for determining the diameter of arteries and veins in a fundus image; [0045]), comprising: acquiring a fundus image ([0122]);
identifying an optic disc region of the fundus image ([0123-124]) and dividing the fundus image into at least three regions comprising a first region (center of the position of the optic disc; [0064]), a second region (first distance; [0064]), and a third region (second distance; [0064]) based on the optic disc region [0123-124];
performing artery and vein segmentation (artery and vein determination unit 440; [0125]) on the fundus image by a deep learning-based (deep neural network model; [0055]) arteriovenous segmentation model (through artery and vein determination unit 440, determines the arteries and veins that meet the preset conditions according to the blood vessel segmentation image in the annular area; [0125]) wherein the arteriovenous segmentation model ([0125]) is trained by a training fundus image (preprocessed fundus image; [0053]) and an arteriovenous vessel annotation result (blood vessels in the fundus image to be detected can be directly segmented based on the deep neural network model, and blood vessel
segmentation image; [0055]) of the training fundus image (preprocessed fundus image; [0053]), and
measuring the lesion features of the hypertensive retinopathy in the fundus image (diameter ratio determination unit 450; [0126]) based on the three regions and the arteriovenous segmentation results (annular region, center of the position of the optic disc, first distance, and second distance; [0064]), the lesion features comprising at least one of arteriovenous cross-indentation features, arteriolar local stenosis features, and arteriolar general stenosis features (narrowing the recognition range, which not only reduces the computational complexity but also increases the accuracy; [0010]).
Jin does not appear to explicitly teach the following limitations wherein the arteriovenous segmentation model is configured to segment an artery and vein in the fundus image to directly acquire an arteriovenous segmentation result, wherein the arteriovenous segmentation results comprise an artery segmentation result and a vein segmentation result, and the arteriovenous vessel annotation result of the training fundus image, the arteriovenous vessel annotation results comprising an artery annotation result and a vein annotation result formed by annotating a boundary of a vessel with a vessel diameter greater than a pre-set vessel diameter in the training fundus image and a small vessel annotation result formed by annotating a direction of a vessel not greater than the pre-set vessel diameter, when a loss function is calculated, a weight of a region corresponding to the small vessel annotation result is adjusted based on the small vessel annotation result.
However, in the same field of endeavor, Badawi teaches a multiloss function based deep convolutional neural network for segmentation of retinal vasculature into arterioles and venules (pg. 1), wherein a deep learning-based (deep encoder-decoder designed based on entire convolutional neural system design, optimized deep learning architecture; pg. 3, col. 2, para. 2) arteriovenous segmentation model (semantic segmentation of retinal vessels simultaneously attaining grouping of arteries and veins; pg. 3, col. 2, para. 2) is configured to segment an artery and vein (achieved semantic classification of vessels to arteries and veins by assigning each pixel of retinal image a class label e.g., arteriole, venule, or background pixel; pg. 3, col. 2, para. 2) in a fundus image (retinal image; pg. 3, col. 2, para. 2; retinal fundus images for AV classification; pg. 6, col. 2, para. 2) to directly acquire an arteriovenous segmentation result (deep learning result comprising AV classification dataset; pg. 3, col. 2, para. 3), wherein the arteriovenous segmentation results (deep learning result comprising AV classification dataset; pg. 3, col. 2, para. 3) comprise an artery segmentation result and a vein segmentation result (AV classification dataset comprising achieved semantic classification of vessels to arteries and veins; pg. 3, col. 2, para. 2-3), and an arteriovenous vessel annotation result (class label e.g., arteriole and venule, gold standard labels of AV classification and vessel segmentation; pg. 3, col. 2, para. 2-3) of a training fundus image (optimized deep CNN trained and evaluated on large newly prepared AV classification dataset of 700 retinal images that helped optimize the learned model and its results; pg. 3, col. 2, para. 3), the arteriovenous vessel annotation results comprising an artery annotation result and a vein annotation result (class label e.g., arteriole and venule, gold standard labels of AV classification and vessel segmentation; pg. 3, col. 2, para. 2-3) formed by annotating a boundary of a vessel with a vessel diameter greater than a pre-set vessel diameter (pixel-to-pixel similarity measure, achieved by comparing pixel from segmented image to same pixel in ground truth and building matrix and performing calculation of desired performance measures; pg. 5, col. 1, para, 2-3) in the training fundus image (optimized deep CNN trained and evaluated on large newly prepared AV classification dataset of 700 retinal images that helped optimize the learned model and its results; pg. 3, col. 2, para. 3) and a small vessel annotation result formed by annotating a direction of a vessel not greater than the pre-set vessel diameter (thin vessels in labelled image defined as vessels that are less than four-pixel width, pixel-to-pixel similarity measure, achieved by comparing pixel from segmented image to same pixel in ground truth and building matrix and performing calculation of desired performance measures; pg. 5, col. 1, para, 2-3), when a loss function is calculated (pixel-wise loss calculation; pg. 5, col. 1, para, 2-3), a weight of a region corresponding to the small vessel annotation result is adjusted based on the small vessel annotation result (thin vessels in labelled image defined as vessels that are less than four-pixel width; pg. 5, col. 1, para, 2-3; adding segment-wise contextual judgment to loss function that optimizes vessel classification, applying optimized deep learning method on newly prepared AV classification dataset, improving accuracy of labels annotated as thin vessels, i.e., deep learning-based pixel level semantic segmentation utilized in classifying retinal blood vessels into arterioles/venules; pg. 2, col. 1, para. 4).
Therefore, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method for determining the diameter of arteries and veins in a fundus image of Jin to further specify the technical features of weighted multi-task segmentation of retinal vessels through a multiloss function optimized deep encoder-decoder/CNN based design for classifying retinal image pixels into arterioles and venules, for the purpose of targeting better optimization of vessel classification, increasing accurate results, reducing bias in sensitivity, fixing artery segment pixels that are wrongly judged as vein pixels, and fixing vein pixels that are judged as arteries in deep learning, as taught by Badawi (pg. 3, col. 2, para. 2-3 and pg. 5, col. 1, para. 4).
Furthermore, and under the principles of inherency, if a prior art device, in its normal and usual operation, would necessarily perform the method claimed, then the method claimed will be considered to be anticipated by the prior art device. When the prior art device is the same as a device described in the specification for carrying out the claimed method, it can be assumed the device will inherently perform the claimed process. In re King, 801 F.2d 1324, 231 USPQ 136 (Fed. Cir. 1986). See MPEP § 2112.02.
With respect to Claim 14, Jin in view of Badawi teaches the measuring method (method for determining the diameter of arteries and veins in a fundus image; [0045]; Jin) according to claim 13, wherein:
the first region (center of the position of the optic disc; [0064]) is a region of a first circle formed by taking a circle center of a circumscribed circle of the optic disc region as the center ([0064]) and a first pre-set multiple (v1) of a diameter of the circumscribed circle as the diameter (optic disc is represented by the positions of multiple points representing the contour of the shape and size of the optic disc; [0064]; Jin);
the second region (first distance; [0064]) is a region from an edge of the first region (center of the position of the optic disc; [0064]) to a second circle formed by taking the circle center as the center (distance between the inner circle of the annular area and the optic disc; [0064]) and a second pre-set multiple (v2) of the diameter of the circumscribed circle as the diameter (optic disc is represented by the positions of multiple points representing the contour of the shape and size of the optic disc; [0064]; Jin);
the third region (second distance; [0064]) is a region from an edge of the second region (first distance; [0064]) to a third circle formed by taking the circle center as the center (distance between the outer circle of the annular area and the optic disc; [0064]) and a third pre-set multiple (v3) of the diameter of the circumscribed circle (optic disc is represented by the positions of multiple points representing the contour of the shape and size of the optic disc; [0064]; Jin); and
v1 < v2 < v3 ([0064]; Jin).
With respect to Claim 15, Jin in view of Badawi teaches the measuring method (method for determining the diameter of arteries and veins in a fundus image; [0045]; Jin) according to claim 14, wherein:
if the arteriovenous cross-indentation features are measured (crossed blood vessels; [0014]), the arteriovenous segmentation results of a fundus region except the first region (center of the position of the optic disc; [0064]) and the second region (first distance; [0064]) are thinned to acquire a first vessel skeleton comprising a plurality of skeleton pixel points taken as first measurement pixel points and to acquire a number of skeleton pixel points within a pre-set range of each of the first measurement pixel points as a first adjacent point number ([0014]; Jin);
pixel points in the arteriovenous segmentation results correspond to the first measurement pixel points of which the number of first adjacent points is greater than a first pre-set number are taken as arteriovenous cross positions ([0014]; Jin);
arteriovenous cross-indentation features are measured based on a ratio of proximal and distal vessel diameters in the arteriovenous segmentation results along the direction of extension of the vein segmentation result and on each of both sides of the arteriovenous cross position ([0014]; Jin); and
the first pre-set number is 3 ([0014]; Jin).
The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met, and thus, “if the arteriovenous cross-indentation features are measured” is not a required step and is merely an optional limitation since the condition(s) precedent is not met. See MPEP § 2111.04 (II).
With respect to Claim 16, Jin in view of Badawi teaches the measuring method (method for determining the diameter of arteries and veins in a fundus image; [0045]; Jin) according to claim 15, wherein:
if the vein segmentation result is discontinuous at the arteriovenous cross position (crossed blood vessels; [0014]) in the arteriovenous segmentation result, a proximal end of each side is a skeleton pixel point on the first vessel skeleton of the vein segmentation result which is closest to the arteriovenous cross position ([0014]; Jin);
if the vein segmentation result is continuous at the arteriovenous cross position in the arteriovenous segmentation result, the proximal end of each side is the arteriovenous cross position ([0014]; Jin);
a distal end of each side is a skeleton pixel point on the first vessel skeleton of the vein segmentation result to which a distance from the arteriovenous cross position is a first pre-set distance ([0014]; Jin); and
the first pre-set distance is 2 to 4 times of a maximum vessel diameter ([0014]; Jin).
The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met, and thus, “if the vein segmentation result is discontinuous/continuous” is not a required step and is merely an optional limitation since the condition(s) precedent is not met. See MPEP § 2111.04 (II).
With respect to Claim 17, Jin in view of Badawi teaches the measuring method (method for determining the diameter of arteries and veins in a fundus image; [0045]; Jin) according to claim 14, wherein:
if the arteriolar local stenosis features are measured (narrowing the recognition range, which not only reduces the computational complexity but also increases the accuracy; [0010]), the artery segmentation result is thinned to acquire a second vessel skeleton comprising a plurality of skeleton pixel points taken as second measurement pixel points ([0010]; Jin);
a number of skeleton pixel points within a pre-set range of each of the second measurement pixel points are acquired as a second adjacent point number, the second measurement pixel points with the number of second adjacent points being greater than the second pre-set number are deleted to obtain a plurality of vessel segments ([0010]; Jin); and
the arteriolar local stenosis features are measured based on a ratio of a minimum vessel diameter to a maximum vessel diameter of each vessel segment ([0010]; Jin).
The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met, and thus, “if the arteriolar local stenosis features are measured” is not a required step and is merely an optional limitation since the condition(s) precedent is not met. See MPEP § 2111.04 (II).
With respect to Claim 18, Jin in view of Badawi teaches the measuring method (method for determining the diameter of arteries and veins in a fundus image; [0045]; Jin) according to claim 17, wherein:
the second pre-set number is 2 ([0010]; Jin);
v1 is 1, v2 is 2, and v3 is 3 ([0010]; Jin); and
the pre-set vessel diameter is 50 μm ([0010]; Jin).
The broadest reasonable interpretation of a method (or process) claim having contingent limitations requires only those steps that must be performed and does not include steps that are not required to be performed because the condition(s) precedent are not met, and thus, “if the arteriolar local stenosis features are measured… wherein: the second pre-set number is 2 ([0010]); v1 is 1, v2 is 2, and v3 is 3 ([0010]); and the pre-set vessel diameter is 50 μm” is not a required step and is merely an optional limitation since the condition(s) precedent is not met. See MPEP § 2111.04 (II).
With respect to Claim 19, Jin in view of Badawi teaches the measuring method (method for determining the diameter of arteries and veins in a fundus image; [0045]; Jin) according to claim 13, wherein the arteriovenous segmentation result is a three-value image (blood vessel segmentation image of the fundus image; [0055], a multi-value image comprising arteries, veins, and background; [0012] & [0053]; Jin).
With respect to Claim 20, Jin in view of Badawi teaches the measuring method (method for determining the diameter of arteries and veins in a fundus image; [0045]; Jin) according to claim 13, wherein the measurement is performed using the vessels with the diameter larger than the pre-set vessel diameter in the arteriovenous segmentation result (the larger the cosine value of the second angle between the two blood vessels in the blood vessel…[0018]; Jin).
With respect to Claim 21, Jin in view of Badawi teaches the measuring method (method for determining the diameter of arteries and veins in a fundus image; [0045]; Jin) according to claim 13, wherein the direction is used to estimate a region corresponding to a vessel not greater than the pre-set vessel diameter ([0018]) and the region is a curve that follows the direction of the vessel not greater than the pre-set vessel diameter (cosine value creating a curve; [0016-18]; Jin).
With respect to Claim 22, Jin in view of Badawi teaches the measuring method (method for determining the diameter of arteries and veins in a fundus image; [0045]; Jin) according to claim 13, wherein:
measuring a vessel diameter comprises performing resolution enhancement on the arteriovenous segmentation result according to a pre-set multiple to generate an enhanced arteriovenous segmentation result ([0099-100]; Jin);
extracting a vessel skeleton in the enhanced arteriovenous segmentation result and fitting the vessel skeleton to obtain a vessel diameter measurement direction of a continuous vessel skeleton and third measurement pixel points, the third measurement pixel points being a plurality of pixel points on the continuous vessel skeleton, and the vessel diameter measurement direction being perpendicular to a tangent line of the continuous vessel skeleton at the third measurement pixel points ([0099-100]; Jin);
using an interpolation algorithm to generate a vessel contour corresponding to the third measurement pixel points based on the enhanced arteriovenous segmentation result, the third measurement pixel points, the vessel diameter measurement direction of the third measurement pixel points, and a pre-set accuracy ([0099-100]; Jin);
calculating a vessel diameter corresponding to the third measurement pixel points based on a number of vessel pixel points in the vessel contour corresponding to the third measurement pixel points, the pre-set multiple, and the pre-set accuracy ([0099-100]; Jin); and
a vessel diameter l corresponding to the third measurement pixel points satisfies: l=n×s⁄e ([0099-100]; Jin);
wherein n is the number of vessel pixel points in the vessel contour corresponding to the third measurement pixel points, s is the pre-set accuracy, and e is the pre-set multiple ([0099-100]; Jin).
With respect to Claim 23, Jin in view of Badawi teaches the measuring method (method for determining the diameter of arteries and veins in a fundus image; [0045]; Jin) according to claim 13, wherein the weight is adjusted to zero (difference between the average values of the red component pixel values of the two blood vessels is less than or equal to 12, inclusive of weight being adjusted to zero; [0100]; Jin).
With respect to Claim 24, Jin in view of Badawi teaches the measuring method (method for determining the diameter of arteries and veins in a fundus image; [0045]; Jin) according to claim 13, wherein the arteriovenous segmentation result further comprises a background segmentation result (blood vessel segmentation image of the fundus image, as shown in fig. 2, wherein the white part in fig. 2 represents the blood vessel and the black part is the background; [0053]; Jin).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Joshi et al. discloses automated method for identification and artery-venous classification of vessel trees in retinal vessel networks substantially similar to that of the claimed invention.
All claims are identical to or patentably indistinct from, or have unity of invention with claims in the application prior to the entry of the submission under 37 CFR 1.114 (that is, restriction (including a lack of unity of invention) would not be proper) and all claims could have been finally rejected on the grounds and art of record in the next Office action if they had been entered in the application prior to entry under 37 CFR 1.114. Accordingly, THIS ACTION IS MADE FINAL even though it is a first action after the filing of a request for continued examination and the submission under 37 CFR 1.114. See MPEP § 706.07(b). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/K MUHAMMAD/Examiner, Art Unit 2872 26 May 2026
/SHARRIEF I BROOME/Primary Examiner, Art Unit 2872
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